Mesoporous Silica Nanoparticles Mediate SiRNA Delivery for Long‐Term Multi‐Gene Silencing in Intact Plants
Bibliographic record
Abstract
RNA interference (RNAi) is a powerful tool for understanding and manipulating signaling pathways in plant science, potentially facilitating the accelerated development of novel plant traits and crop yield improvement. The common strategy for delivering siRNA into intact plants using agrobacterium or viruses is complicated and time-consuming, limiting the application of RNAi in plant research. Here, a novel delivery method based on mesoporous silica nanoparticles (MSNs) is reported, which allows for the efficient delivery of siRNA into mature plant leaves via topical application without the aid of mechanical forces, achieving transient gene knockdown with up to 98% silencing efficiency at the molecular level. In addition, this method is nontoxic to plant leaves, enabling the repeated delivery of siRNA for long-term silencing. White spots and yellowing phenotypes are observed after spraying the MSN-siRNA complex targeted at phytoene desaturase and magnesium chelatase genes. After high light treatment, photobleaching phenotypes are also observed by spraying MSNs-siRNA targeted at genes into the Photosystem II repair cycle. Furthermore, the study demonstrated that MSNs can simultaneously silence multiple genes. The results suggest that MSN-mediated siRNA delivery is an effective tool for long-term multi-gene silencing, with great potential for application in plant functional genomic analyses and crop improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".